A
licenseA
qualityD
maintenanceAn MCP server that enables LLMs to generate spoken audio from text using OpenAI's Text-to-Speech API, supporting various voices, models, and audio formats.
1
13 npm
1
MIT
Audio Transcriber MCP Server
by Ichigo3766
OpenAI 음성-텍스트 변환 MCP 서버
OpenAI의 API를 사용하여 오디오 전사 기능을 제공하는 MCP 서버입니다.
설치
설정
저장소를 복제합니다.
지엑스피1
종속성 설치:
npm install서버를 빌드하세요:
npm run build환경 변수에 OpenAI API 키를 설정합니다.
환경에 서버 구성을 추가합니다.
{
"mcpServers": {
"audio-transcriber": {
"command": "node",
"args": [
"/path/to/audio-transcriber-mcp/build/index.js"
],
"env": {
"OPENAI_API_KEY": "",
"OPENAI_BASE_URL": "", // Optional
"OPENAI_MODEL": "" // Optional
}
}
}
}/path/to/audio-transcriber-mcp 저장소를 복제한 실제 경로로 바꾸세요.
Related MCP server: whisper-telegram-mcp
특징
도구
transcribe_audio- OpenAI의 API를 사용하여 오디오 파일을 변환합니다.파일 경로를 필수 매개변수로 사용합니다.
선택 매개변수:
save_to_file: 파일에 전사본을 저장할지 여부를 지정하는 부울 값입니다.
언어: ISO-639-1 언어 코드(예: "en", "es")
특허
이 MCP 서버는 MIT 라이선스에 따라 라이선스가 부여됩니다. 즉, MIT 라이선스의 조건에 따라 소프트웨어를 자유롭게 사용, 수정 및 배포할 수 있습니다. 자세한 내용은 프로젝트 저장소의 LICENSE 파일을 참조하세요.
Available Tools
1 tooltranscribe_audioC
Transcribe an audio file using OpenAI Whisper API
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path to the audio file | |
| language | No | Language of the audio in ISO-639-1 format (e.g. "en", "es"). Default is "en". | |
| save_to_file | No | Whether to save the transcription to a file next to the audio file |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions the API but doesn't disclose key behavioral traits: whether it's read-only or mutative, error handling, rate limits, authentication needs, or what happens with the 'save_to_file' option. The description is minimal and misses critical operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It's front-loaded with the core purpose and implementation detail. Every word earns its place, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., transcription text, file path), error conditions, or behavioral details. For a tool with 3 parameters and potential side effects (saving files), more context is needed for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters. The description adds no additional meaning beyond implying audio file processing. It doesn't explain parameter interactions or provide examples, so it meets the baseline but doesn't enhance understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Transcribe') and resource ('an audio file'), specifying the implementation method ('using OpenAI Whisper API'). It's specific enough to understand the core function, though without sibling tools, differentiation isn't applicable. The purpose is unambiguous but could be slightly more detailed about output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or typical use cases. It mentions the API but doesn't explain limitations or ideal scenarios. With no sibling tools, this is less critical, but still lacks context for effective agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
transcribe_audio
TDQS
B3.1/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and singular, making it impossible for an agent to misselect between non-existent alternatives.
Naming Consistency5/5
The single tool name follows a clear verb_noun pattern (transcribe_audio), and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and aligns with common conventions.
Tool Count2/5
A single tool is too few for a server named 'Audio Transcriber MCP Server', as it suggests a limited scope that may not cover related operations like transcription status checks, file management, or configuration. This minimal set could hinder agent workflows that require more comprehensive functionality.
Completeness2/5
The tool set is severely incomplete for audio transcription tasks. While it provides a core transcription function, it lacks essential operations such as listing available transcriptions, retrieving transcription results, handling errors, or managing audio files, which are typical in such domains and could lead to agent failures.
Maintenance
ActivityInactive
ResponsivenessResponsive
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